Application of Table Tennis Ball Trajectory and Rotation-Oriented Prediction Algorithm Using Artificial Intelligence

Abstract

The present work aims to accelerate sports development in China and promote technological innovation in the artificial intelligence (AI) field. After analyzing the application and development of AI, it is introduced into sports and applied to table tennis competitions and training. The principle of the trajectory prediction of the table tennis ball (TTB) based on AI is briefly introduced. It is found that the difficulty of predicting TTB trajectories lies in rotation measurement. Accordingly, the rotation and trajectory of TTB are predicted using some AI algorithms. Specifically, a TTB detection algorithm is designed based on the Feature Fusion Network (FFN). For feature exaction, the cross-layer connection network is used to strengthen the learning ability of convolutional neural networks (CNNs) and streamline network parameters to improve the network detection response. The experimental results demonstrate that the trained CNN can reach a detection accuracy of over 98%, with a detection response within 5.3 ms, meeting the requirements of the robot vision system of the table tennis robot. By comparison, the traditional Color Segmentation Algorithm has advantages in detection response, with unsatisfactory detection accuracy, especially against TTB's color changes. Thus, the algorithm reported here can immediately hit the ball with high accuracy. The research content provides a reference for applying AI to TTB trajectory and rotation prediction and has significant value in popularizing table tennis.